Gross tumor volume (GTV) regions of lung tumors should be determined with repeatability and reproducibility on planning computed tomography (CT) in radiation treatment planning to reduce intra- and inter-observer variations of GTV regions. Therefore, we have attempted to develop an automated segmentation framework of the GTV regions on planning CT images using dense V-Net deep learning (DenseVDL). In order to evaluate the GTV regions extracted by the DenseVDL network, Dice similarity coefficient (DSC) was used in this study. The proposed framework achieved average 2D-DSC of 0.73 and 3D-DSC of 0.76 for sixteen cases. The proposed framework using the DenseVDL may be useful for assisting in radiation treatment planning for lung cancer.
Our aim was to develop a Bayesian delineation framework of clinical target volumes (CTVs) for prostate cancer radiotherapy using an anatomical-features-based machine learning (AF-ML) technique. Probabilistic atlases (PAs) of the pelvic bone and the CTV were generated from 43 training cases. Translation vectors, which could move the CTV PAs to CTV locations, were estimated using the AF-ML after a bone-based registration between the PAs and planning computed tomography (CT) images. An input vector derived from 11 AF points was fed to three AF-ML techniques (artificial neural network: ANN, random forest: RF, support vector machine: SVM). The AF points were selected from edge points and centroids of anatomical structures around prostate. Reference translation vectors between centroids of CTV PAs and CTVs were given to the AF-ML as teaching data. The CTV regions were extracted by thresholding posterior probabilities produced by using the Bayesian inference with the translated CTV PA and likelihoods of planning CT values. The framework was evaluated based on a leave-one-out test with CTV contours determined by radiation oncologists. Average location errors of CTV PAs along the anterior-posterior and superior-inferior directions without AF-ML were 5.7±4.6 mm and 5.5±4.3 mm, respectively, whereas the errors along the two directions with ANN, which showed the best performance, were 2.4±1.7 mm and 2.2±2.2 mm, respectively. The average Dice’s similarity coefficient between reference and estimated CTVs for 44 test cases were 0.81±0.062 with ANN. The framework using AF-ML could accurately estimate CTVs of prostate cancer radiotherapy.
The goal of our study was to develop a computational framework for reconstruction of four-dimensional computed
tomography (4D-CT) images during treatment time using electronic portal imaging device (EPID) images based on a
dynamic 2D/3D registration. The 4D-CT images during treatment time (“treatment” 4D-CT images) were reconstructed
by performing an affine transformation-based dynamic 2D/3D registration between dynamic clinical portal dose images
(PDIs) derived from the EPID images with planning CT images through planning PDIs for all frames. Elements of the
affine transformation matrices (transformation parameters) were optimized using a Levenberg-Marquardt (LM)
algorithm so that the planning PDIs could be similar to the dynamic clinical PDIs for all frames. Initial transformation
parameters in each frame should be determined for finding optimum transformation parameters in the LM algorithm. In
this study, the optimum transformation parameters in a frame employed as the initial transformation parameters for
optimizing the transformation parameter in the consecutive frame. Gamma pass rates (3 mm/3%) were calculated for
evaluating a similarity of the dose distributions between the dynamic clinical PDIs and “treatment” PDIs, which were
calculated from “treatment” 4D-CT images, for all frames. The framework was applied to eight lung cancer patients who
were treated with stereotactic body radiation therapy (SBRT). A mean of the average gamma pass rates between the
dynamic clinical PDIs and the “treatment” PDIs for all frames was 98.3±1.2% for eight cases. In conclusion, the
proposed framework makes it possible to dynamically monitor patients’ movement during treatment time.
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